scholarly journals Summarization and Sentiment Analysis for Financial News

2021 ◽  
Vol 9 (10) ◽  
pp. 88-90
Author(s):  
Anusha Kalbande
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Financial Analysts ◽  
Relevant Information ◽  
Business Leaders ◽  
Financial News ◽  
Web Scraping ◽  
Abstract Data ◽  
News Summarization ◽  
Online Sources

Abstract: Data is growing at an unimaginable speed around us, but what part of it is really useful information? Business leaders, financial analysts, stock market enthusiasts, researchers etc. often need to go through a plethora of news articles and data every day, and this time spent may not even result in any fruitful insights. Considering such a huge volume of data, there is difficulty in gaining precise, relevant information and interpreting the overall sentiment portrayed by the article. The proposed method helps in conceptualizing a tool that takes financial news from selected and trusted online sources as an input and gives a summary of the same along with a basic positive, negative or neutral sentiment. Here it is assumed that the tool user is familiar with the company’s profile. Based on the input (company name/symbol) given by the user, the corresponding news articles will be fetched using web scraping. All these articles will then be summarized to gain succinct and to the point information. An overall sentiment about the company will be portrayed based on the different important features in the article about the company. Keywords: Financial News; Summarization; Sentiment Analysis.

The association between stock price volatility and financial news – a sentiment analysis approach

Kybernetes ◽  
2017 ◽  
Vol 46 (8) ◽  
pp. 1341-1365 ◽  
Author(s):  
Jia-Lang Seng ◽  
Hsiao-Fang Yang
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Returns ◽  
Price Volatility ◽  
Model Verification ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Content Type ◽  
Financial News ◽  
Depth Analysis

Purpose The purpose of this study is to develop the dictionary with grammar and multiword structure has to be used in conjunction with sentiment analysis to investigate the relationship between financial news and stock market volatility. Design/methodology/approach An algorithm has been developed for calculating the sentiment orientation and score of data with added information, and the results of calculation have been integrated to construct an empirical model for calculating stock market volatility. Findings The experimental results reveal a statistically significant relationship between financial news and stock market volatility. Moreover, positive (negative) news is found to be positively (negatively) correlated with positive stock returns, and the score of added information of the news is positively correlated with stock returns. Model verification and stock market volatility predictions are verified over four time periods (monthly, quarterly, semiannually and annually). The results show that the prediction accuracy of the models approaches 66% and stock market volatility with a particular trend-predicting effect in specific periods by using moving window evaluation. Research limitations/implications Only one news source is used and the research period is only two years; thus, future studies should incorporate several data sources and use a longer period to conduct a more in-depth analysis. Practical implications Understanding trends in stock market volatility can decrease risk and increase profit from investment. Therefore, individuals or businesses can feasibly engage in investment activities for profit by understanding volatility trends in capital markets. Originality/value The ability to exploit textual information could potentially increase the quality of the data. Few scholars have applied sentiment analysis in investigating interdisciplinary topics that cover information management technology, accounting and finance. Furthermore, few studies have provided support for structured and unstructured data. In this paper, the efficiency of providing the algorithm, the model and the trend in stock market volatility has been demonstrated.

scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

2021 ◽  
Vol 18 (12) ◽  
pp. 6408
Author(s):  
Mario Jojoa Acosta ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel de la Torre Díez ◽  
Manuel Franco-Martín
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

scholarly journals Predicting Stock Market Indexes, Two Days In Advance, Using NewsInn Algorithm

2015 ◽  
Vol 5 (2) ◽  
pp. 308
Author(s):  
Radu Nicoara
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Markets ◽  
News Article ◽  
World News ◽  
Opinion Extraction ◽  
Extraction Algorithm ◽  
Market Indexes

<p class="ber"><span lang="EN-GB">NewsInn is an A.I. Driven Algorithm that processes and conglomerates news from major news publications. It uses an opinion extraction algorithm to do a sentiment analysis on every news article. </span></p><p class="ber"><span lang="EN-GB">Considering that stock markets are heavily influenced be world news, we conducted a study to show the link between the detected sentiment inside the news, and the most used Stock Market Indexes: S&amp;P 500, Dow Jones and NASDAQ. Results showed an almost 70.00% accuracy in predicting market fluctuation two days in advance.</span></p>

scholarly journals An Approach for Big Data to Evolve the Auspicious Information from Cross-Domains

2017 ◽  
Vol 7 (2) ◽  
pp. 967 ◽  
Author(s):  
Preeti Arora ◽  
Deepali Virmani ◽  
P.S. Kulkarni
Keyword(s):  
Big Data ◽  
Sentiment Analysis ◽  
Relevant Information ◽  
Domain Analysis ◽  
The Self ◽  
The Other ◽  
Classification Approach ◽  
Cross Domain ◽  
The Cross

Sentiment analysis is the pre-eminent technology to extract the relevant information from the data domain. In this paper cross domain sentimental classification approach Cross_BOMEST is proposed. Proposed approach will extract <strong>†</strong>ve words using existing BOMEST technique, with the help of Ms Word Introp, Cross_BOMEST determines <strong>†</strong>ve words and replaces all its synonyms to escalate the polarity and blends two different domains and detects all the self-sufficient words. Proposed Algorithm is executed on Amazon datasets where two different domains are trained to analyze sentiments of the reviews of the other remaining domain. Proposed approach contributes propitious results in the cross domain analysis and accuracy of 92 % is obtained. Precision and Recall of BOMEST is improved by 16% and 7% respectively by the Cross_BOMEST.

scholarly journals IFRS and Stock Returns: An Empirical Analysis in Brazil

2016 ◽  
Vol 7 (2) ◽  
pp. 179 ◽  
Author(s):  
Rodrigo F. Malaquias ◽  
Anderson Martins Cardoso ◽  
Gabriel Alves Martins
Keyword(s):  
New York ◽  
Stock Market ◽  
Stock Returns ◽  
Stock Exchange ◽  
Relevant Information ◽  
Graphic Analysis ◽  
Accounting Numbers ◽  
Before And After ◽  
Accounting Reports ◽  
Accounting Convergence

In recent years, the convergence of accounting standards has been an issue that motivated new studies in the accounting field. It is expected that the convergence provides users, especially external users of accounting information, with comparable reports among different economies. Considering this scenario, this article was developed in order to compare the effect of accounting numbers on the stock market before and after the accounting convergence in Brazil. The sample of the study involved Brazilian listed companies at BM&FBOVESPA that had American Depository Receipts (levels II and III) at the New York Stock Exchange (NYSE). For data analysis, descriptive statistics and graphic analysis were employed in order to analyze the behavior of stock returns around the publication dates. The main results indicate that the stock market reacts to the accounting reports. Therefore, the accounting numbers contain relevant information for the decision making of investors in the stock market. Moreover, it is observed that after the accounting convergence, the stock returns of the companies seem to present lower volatility.

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